Automation has become part of the global manufacturing line, where robots take on repetitive jobs, like filling boxes or welding a car frame in the same way, day after day. But what if robots could step away from their limited range of tasks, and start to problem solve in complex operational situations, like spotting a malfunction on the assembly line or identifying a better compound for a part? And how could robots enabled with "deep learning" – where algorithms learn from large amounts of data collected via experience – begin to share insights with other robots, to increase innovation in all kinds of settings, from factories to self-driving cars on the road to early cancer detection and drug discovery in hospitals? These questions are the focus of Preferred Networks, a cutting-edge artificial intelligence company founded in 2014. The Tokyo-based firm, which is worth roughly $2 billion, according to CB Insights, is a symbol of Japan's sweeping strategic innovation initiative, where AI and robotics are viewed as keys to both solving social issues and achieving new economic growth.

NASA's ambitious plans to build a base on the surface of the moon will likely be delayed. According to NASA's Dough Loverro, who oversees the agency's human exploration programs, several aspects of the project's technical design and multi-phase rollout need to be revised. One of the first changes will affect NASA's touted Lunar Gateway, a space station planned to orbit the moon and to be used as a staging point for the subsequent construction of a base on the moon's surface. NASA's ambitious plans for a lunar base will be delayed by at least a year after unexpected technical complications with the Lunar Gateway, a space station planned to orbit the moon and used as a staging area for construction materials NASA had targeted a completion window for the Lunar Gateway in 2024, and promised construction on the lunar base would begin no later than 2025, but according to a report in the Wall Street Journal, the Lunar Gateway is being reworked. NASA says it will still have a space station in orbit around the moon in 2024, but it won't initially be as capable as originally planned, likely delaying the completion date for the lunar base.

The concept of "smart cities" is no longer confined to the realms of futuristic science fiction--they're quickly becoming part of our everyday reality. Technologies like self-driving buses that communicate with traffic lights and AI-monitored CCTV cameras are being implemented in cities from Singapore to Las Vegas, and the technology behind these smart-city initiatives promises innovative solutions for both municipalities and their citizens--offering safer and more efficient living for an ever-growing population. The smart-city promise is often delivered without the fine print though: namely, that a single attack waged against just one component of a connected infrastructure could disable an entire smart city in a matter of minutes. The attack could come from a single line of code. This looming threat is turning the promises of revolutionized living standards into a potential menace to public safety.

In this paper, we present new results on the fair and efficient allocation of indivisible goods to agents that have monotone, submodular, non-additive valuation functions over bundles. Despite their simple structure, these agent valuations are a natural model for several real-world domains. We show that, if such a valuation function has binary marginal gains, a socially optimal (i.e. utilitarian social welfare-maximizing) allocation that achieves envy-freeness up to one item (EF1) exists and is computationally tractable. We also prove that the Nash welfare-maximizing and the leximin allocations both exhibit this fairness-efficiency combination, by showing that they can be achieved by minimizing any symmetric strictly convex function over utilitarian optimal outcomes. To the best of our knowledge, this is the first valuation function class not subsumed by additive valuations for which it has been established that an allocation maximizing Nash welfare is EF1. Moreover, for a subclass of these valuation functions based on maximum (unweighted) bipartite matching, we show that a leximin allocation can be computed in polynomial time.

We describe nonparametric deconvolution models (NDMs), a family of Bayesian nonparametric models for collections of data in which each observation is the average over the features from heterogeneous particles. For example, these types of data are found in elections, where we observe precinct-level vote tallies (observations) of individual citizens' votes (particles) across each of the candidates or ballot measures (features), where each voter is part of a specific voter cohort or demographic (factor). Like the hierarchical Dirichlet process, NDMs rely on two tiers of Dirichlet processes to explain the data with an unknown number of latent factors; each observation is modeled as a weighted average of these latent factors. Unlike existing models, NDMs recover how factor distributions vary locally for each observation. This uniquely allows NDMs both to deconvolve each observation into its constituent factors, and also to describe how the factor distributions specific to each observation vary across observations and deviate from the corresponding global factors. We present variational inference techniques for this family of models and study its performance on simulated data and voting data from California. We show that including local factors improves estimates of global factors and provides a novel scaffold for exploring data.

Label shift describes the setting where although the label distribution might change between the source and target domains, the class-conditional probabilities (of data given a label) do not. There are two dominant approaches for estimating the label marginal. BBSE, a moment-matching approach based on confusion matrices, is provably consistent and provides interpretable error bounds. However, a maximum likelihood estimation approach, which we call MLLS, dominates empirically. In this paper, we present a unified view of the two methods and the first theoretical characterization of the likelihood-based estimator. Our contributions include (i) conditions for consistency of MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified view of the methods, casting the confusion matrix as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting the impacts of miscalibration and estimation error. Our analysis attributes BBSE's statistical inefficiency to a loss of information due to coarse calibration. We support our findings with experiments on both synthetic data and the MNIST and CIFAR10 image recognition datasets.

A standard method for testing some of the mechanical properties of materials is to poke them with a sharp point. This "indentation technique" can provide detailed measurements of how the material responds to the point's force, as a function of its penetration depth. With advances in nanotechnology during the past two decades, the indentation force can be measured to a resolution on the order of one-billionth of a Newton (a measure of the force approximately equivalent to the force you feel when you hold a medium-sized apple in your hand), and the sharp tip's penetration depth can be captured to a resolution as small as a nanometer, or about 1/100,000 the diameter of a human hair. Such instrumented nanoindentation tools have provided new opportunities for probing physical properties in a wide variety of materials, including metals and alloys, plastics, ceramics, and semiconductors. But while indentation techniques, including nanoindentation, work well for measuring some properties, they exhibit large errors when probing plastic properties of materials -- the kind of permanent deformation that happens, for example, if you press your thumb into a piece of silly putty and leave a dent, or when you permanently bend a paper clip using your fingers.

The technology to power connected cities exists today--and continued growth is predicted. Will all our cities soon be connected? Or do hurdles stand in the way? Perhaps one of the biggest challenge will be overcoming regulatory hurdles that could slow the progress down. Technavio says the autonomous bus market, as an example, will grow by 2364 units during 2020 and 2024, which is a growth rate of 32%.

Today, researchers and leaders from the Allen Institute for AI, Chan Zuckerberg Initiative (CZI), Georgetown University's Center for Security and Emerging Technology (CSET), Microsoft, and the National Library of Medicine (NLM) at the National Institutes of Health released the COVID-19 Open Research Dataset (CORD-19) of scholarly literature about COVID-19, SARS-CoV-2, and the Coronavirus group. Requested by The White House Office of Science and Technology Policy, the dataset represents the most extensive machine-readable Coronavirus literature collection available for data and text mining to date, with over 29,000 articles, more than 13,000 of which have full text. Now, The White House joins these institutions in issuing a call to action to the Nation's artificial intelligence experts to develop new text and data mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The collection was constructed via a unique collaboration between Microsoft, NLM, CZI, and the Allen Institute for AI, coordinated by Georgetown University. Microsoft's web-scale literature curation tools were used to identify and bring together worldwide scientific efforts and results, CZI provided access to pre-publication content, NLM provided access to literature content, and the Allen AI team transformed the content into machine-readable form, making the corpus ready for analysis and study.

A mushroom cloud explosion in the New Mexico desert on July 16, 1945 forever changed the nature of warfare. Science had given birth to weapons so powerful they could end humanity. To survive, the United States had to develop new strategies and policies that responsibly limited nuclear weapon proliferation and use. Warfare is again changing as modern militaries integrate autonomous and semiautonomous weapon systems into their arsenals. The United States must act swiftly to maximize the potential of these new technologies or risk losing its dominance.